A population-based atlas of the human pyramidal tract in 410 healthy participants
Bibliographic record
Abstract
Abstract With the advances in diffusion MRI and tractography, numerous atlases of the human pyramidal tract (PyT) have been proposed but the inherent limitation of tractography to resolve crossing bundles within the centrum semiovale have so far prevented the complete description of the most lateral PyT projections. Here, we combined a precise manual positioning of individual subcortical regions of interest along the descending pathway of the PyT with a new bundle-specific tractography algorithm. This later is based on anatomical priors to improve streamlines tracking in crossing areas. We then extracted both left and right PyT in a large cohort of 410 healthy participants and built a population-based atlas of the whole-fanning PyT with a complete description of its most cortico-lateral projections. Clinical applications are envisaged, the whole-fanning PyT atlas being likely a better marker of corticospinal integrity metrics than those currently used within the frame of prediction of post-stroke motor recovery. The present population-based PyT, freely available, provides an interesting tool for clinical applications in order to locate specific PyT damage and its impact to the short and long-term motor recovery after stroke.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".